Formulation of Separation Distance to Mitigate Wind-Induced Pounding of Tall Buildings
Bibliographic record
Abstract
Structures in proximity subjected to a substantial lateral load (e.g., wind and earthquakes) can lead to a significant hazard known as structural pounding. If not properly mitigated, such impacts can lead to local and global damage (i.e., structural failure). Mitigation approaches can include providing a suitable separation gap distance between structures, installing adequate shock absorbers, or designing the structure for the additional pounding impact loads. Wind-induced pounding of structures can be of higher risk to buildings due to large deflections developed during wind events. The current study develops various mathematical formulas to determine the suitable separation distance between structures in proximity to avoid pounding. The developed procedure relies first on wind-load evaluations using Large Eddy Simulation (LES) models. Then, the extracted wind loads from the LES are applied to finite element method models to determine the building deflections. Various building heights, wind velocities, and flexibility levels are examined to prepare a training database for developing the mathematical formulas. A genetic algorithm is utilised to correlate the required separation gap distance to the varying parameters of the tall buildings. It was found that more complex formulas can achieve better mapping to the training database.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".